Planning and decision making under uncertainty are fundamental requirements for autonomous robots. Uncertainties often arise due to insufficient knowledge about the environment, imperfect sensing and inexact robot motion. In these conditions, the robot poses or other variables of interest can only be dealt with in terms of probabilities. Planning is therefore performed in the belief space, which corresponds to the set of all probability distributions over possible robot states [1]}. At a given time instant, we consider the belief or the belief state of the robot which corresponds to a probability distribution of the robot state (or other variables of interest) given the measurements and controls thus far [2]}. Consequently, for efficient planning and decision making, it is required to reason about future belief distributions due to candidate actions and the corresponding expected observations. Such a problem falls under the category of Partially Observable Markov Decision Processes (POMDPs) [3]}.
